Automatic meter reading (AMR) systems are generally known in the art. Utility companies, for example, use AMR systems to read and monitor customer meters remotely, typically using radio frequency (RF) communication. AMR systems are favored by utility companies and others who use them because they increase the efficiency and accuracy of collecting readings and managing customer billing. For example, utilizing an AMR system for the monthly reading of residential gas, electric, or water meters eliminates the need for a utility employee to physically enter each residence or business where a meter is located to transcribe a meter reading by hand.
There are several different ways in which some current AMR systems are configured, including fixed network and mobile network systems. In a fixed network, encoder-receiver-transmitter (ERT) type endpoint devices at meter locations communicate with readers that collect readings and data using RF communication. There may be multiple fixed intermediate readers located throughout a larger geographic area on utility poles, for example, with each endpoint device associated with a particular reader and each reader in turn communicating with a central system. Other fixed systems can utilize a system including repeaters or relay devices that expand the coverage area for each reader, cell control units (CCUs) that concentrate data and forward the same on to the system head end using a wide area network (WAN), or other suitable communication infrastructure. In simple fixed systems, only one central reader may be utilized with all of the endpoint devices. In a mobile network AMR environment, a handheld, vehicle-mounted, or otherwise mobile reader device with RF communication capabilities is used to collect data from endpoint devices as the mobile reader is moved from place to place.
One design criterion for utility meter reading systems involves meter data management (MDM) systems that generally involve a centralized processing model. Such centralized processing models, however, are subject to various problems including, but not limited to, system performance, scalability, data latency, fault tolerance, complexity, infrastructure cost, and batch processing issues.
For example, performance throughput and scalability can generally only be maintained by providing parallel processing technology. Data latency is becoming more of an issue as expectations are rapidly approaching real-time. Fault tolerance requirements demand that sufficient computing power, including provision of disaster recovery sites, be provided. A whole host of concerns arise from commonly used centralized solutions including complexity, the high cost of powerful computer infrastructure, and the inherent requirements imposed on centralized solutions from batch processing of data and other infrastructure communications requirements.
In view of such concerns, it would be advantageous, therefore, to provide methodologies and associated apparatuses/devices wherein failure of critical components may be quickly identified.